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P1 Data-Linkage Between Administrative and Pathological Anatomy Databases for the Identification of Lung Cancer Patients Eligible to Innovative Therapies

      Objectives

      The introduction of innovative and costly oncological therapies highlights the need for healthcare system to balance the innovation with the access to therapies based on patient eligibility. This study aimed to identify, through real-world data from administrative and pathological anatomy (PA) databases, patients with metastatic lung cancer carrying specific tumor markers, potentially eligible to innovative immunotherapy treatments.

      Methods

      The data-linkage between administrative and PA databases on a pool of Italian local Healthcare Entities (LHEs) was performed. Data were reported per million of health-assisted individuals. All patients diagnosed for lung cancer (ICD-9-CM code:162) from 2013-2019 were included. Metastatic disease has been diagnosed by using ICD-9-CM codes 196-197-198. The data integration with PA data-set was carried out to evaluate morphologic characteristics (M-80023; M-80413; M-80423) and the level of PD-L1 expression, required for the eligibility to specific immunotherapies.

      Results

      Overall, 4,387 lung cancer patients were included, with annual incidence of 0.7/1,000 health-assisted individuals for year 2019. Among them, 37% (N=1,625) presented metastasis, in line with published evidence indicating around 30-40% of lung cancer to present metastasis. Further analyses were performed in metastatic patients with a record in the PA database (N=365). The 87% (N=317) of them had non-small cell lung cancer, accordingly to the literature estimation, and 79% of patients were potentially eligible for immunotherapy since showed positivity to PD-L1 TPS ≥1%.

      Conclusions

      The present study results are in line with epidemiological data reported by AIRTUM (incidence 0.7/1,000) and with international literature and showed how the applied methodology could represent a valuable tool for identifying patients eligible to new therapies. The use of PA data-set could allow the detection of patients with specific genetic profiles and their access to innovative medications. The quantification of potentially eligible patients would also allow budget impact estimation needed to plan the pharmaceutical spending by LHE.